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Predicting the time and effort for a software problem has long been a difficult task. We present an approach that automatically predicts the fixing effort, i.e., the person-hours spent on fixing an issue. Our technique leverages existing issue tracking systems: given a new issue report, we use the Lucene framework to search for similar, earlier reports and use their average time as a prediction. Our approach thus allows for early effort estimation, helping in assigning issues and scheduling stable releases. We evaluated our approach using effort data from the JBoss project. Given a sufficient number of issues reports, our automatic predictions are close to the actual effort; for issues that are bugs, we are off by only one hour, beating naive predictions by a factor of four.